Biomedical Event Extraction (BEE) is a demanding and prominent technology that attracts the researchers and scientists in the field of natural language processing (NLP). The conventional method relies mostly on external NLP packages and manual designed features, where the features engineering is complex and large. In addition, the conventional methods on BEE uses a pipeline process that splits a task into many subtasks, however, the relationship between these sub-tasks is not defined. In this paper, such limitations are avoided using the combination technique that relies on Capsule Network (CapsNet) to perform a task. The CapsNet is used for the extraction of feature representation from the input corpora and then the combination technique reconstructs the events from RNN output. This method extracts the tasks from a BEE over several annotated corpora that extract the events from the molecular level in case of multi-level events. The proposed model is compared with state-of-the-art models over various text corpora datasets. The results show an improved rate of accuracy of CapsNet classification over cancer biomedical events than the existing methods.

OFDM is a ubiquitous modulation scheme used to achieve high data rate during transmission and reception in broadband internet of things application. But the envelope aberration ofOFDMsignal leads to high peakto-average power ratio (PAPR) which finally results in overall transmitter inefficiency. This article proposes a neural network (NN) based gradient clipping approach at the transmitter and a linear regression model at the receiver to minimize the PAPR in OFDMsystems with reasonable computational complexity. The simulation result shows that when compared to the original OFDM signal, the proposed Neural network-based Regression assisted PAPR Reduction achieves PAPR reduction by 82.9% and 84.1% for 64QAM-OFDM and 16QAM OFDM signal respectivelywithout compromising on the bit error rate (BER) performance.